Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI2
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	27202	8	18477	9	11236	12	8139	13	11613	10	2964	15	79632	4
	fir	11596	7	6078	10	3089	14	590	32	366	28	680	32	22399	5
	pine	1323	26	458	54	108	62	3885	23	2410	24	81	100	8266	14
	larch	316	35	54	58	299	57	3023	18	1064	27	1924	22	6681	12
	Arolla pine	0	.	0	.	0	.	480	29	966	27	16	100	1462	20
	other conifers	812	28	53	58	194	39	277	82	247	51	0	.	1583	22
	beech	20782	5	7121	12	5190	12	1144	33	889	29	6485	15	41611	5
	maple	4302	10	2531	17	1601	18	636	30	289	48	348	32	9708	7
	ash	3941	13	1465	20	1734	23	508	35	418	66	1018	28	9085	9
	oak	3529	20	348	39	39	71	528	32	15	100	1493	32	5953	15
	sweet chestnut	17	100	0	.	0	.	19	100	0	.	8922	15	8957	15
	other broadleaves	7398	10	4045	13	3115	19	3378	18	3205	23	10126	12	31266	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	81218	4	40632	6	26605	8	22609	9	21482	8	34057	6	226602	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12849	7	12925	8	7553	12	5046	12	8066	9	2583	15	49021	4
	fir	6874	9	3319	11	1354	15	306	37	175	45	272	36	12301	6
	pine	1158	20	263	69	245	53	1715	21	1325	23	86	60	4790	12
	larch	345	32	55	74	89	72	2212	17	1075	21	1339	18	5117	10
	Arolla pine	0	.	0	.	0	.	456	36	746	25	0	.	1202	20
	other conifers	325	36	17	100	15	100	0	.	17	100	0	.	375	32
	beech	14248	6	4250	14	3347	14	640	33	597	31	2767	17	25849	5
	maple	3159	10	1446	16	1496	18	149	47	88	45	150	40	6488	7
	ash	2901	11	960	28	1381	21	164	48	185	56	260	33	5850	9
	oak	2227	16	106	46	94	65	108	57	15	100	507	31	3057	13
	sweet chestnut	0	.	0	.	0	.	93	72	15	100	3200	16	3308	16
	other broadleaves	2606	12	820	21	1019	22	493	28	531	37	3177	13	8647	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	46692	3	24160	6	16594	8	11381	8	12837	8	14342	6	126005	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10014	6	8330	7	5092	11	3030	13	5566	9	1892	15	33924	3
	fir	4962	8	2205	12	1165	15	230	48	112	41	171	38	8845	6
	pine	1768	13	147	50	144	43	766	20	748	23	103	59	3677	9
	larch	260	35	51	75	25	77	1262	17	843	19	854	21	3296	10
	Arolla pine	0	.	15	71	0	.	257	29	403	26	0	.	675	19
	other conifers	142	51	0	.	0	.	0	.	0	.	8	100	150	49
	beech	9299	6	2262	12	2206	13	177	62	261	35	979	18	15185	5
	maple	1163	12	570	22	729	16	44	71	51	63	25	100	2582	9
	ash	2191	15	305	26	453	23	32	83	34	64	19	72	3033	12
	oak	1127	14	138	86	7	100	19	100	12	100	179	41	1483	14
	sweet chestnut	0	.	0	.	0	.	42	70	6	100	801	20	849	19
	other broadleaves	1139	14	175	35	233	31	157	43	140	44	729	21	2573	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	32065	3	14199	6	10055	7	6016	9	8177	8	5760	7	76272	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7505	5	5796	7	3209	9	1623	11	3353	9	1168	14	22655	3
	fir	3340	7	1685	11	892	15	134	41	116	32	222	31	6388	5
	pine	1159	12	207	48	42	54	207	33	400	23	28	61	2044	10
	larch	133	25	63	47	12	70	1031	15	822	14	511	18	2574	8
	Arolla pine	0	.	0	.	0	.	147	40	219	26	0	.	366	22
	other conifers	74	40	0	.	0	.	0	.	0	.	0	.	74	40
	beech	4992	6	1209	14	1035	15	49	62	180	33	393	19	7858	5
	maple	353	16	171	23	239	23	6	100	14	71	8	100	791	11
	ash	926	14	90	31	167	24	7	100	6	100	25	59	1222	11
	oak	640	16	0	.	7	100	0	.	41	76	74	64	762	15
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	369	23	376	23
	other broadleaves	298	21	104	52	84	40	30	62	14	71	153	25	684	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	19419	3	9326	5	5687	7	3243	9	5166	7	2952	8	45793	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3955	6	2890	8	1795	10	727	15	1564	10	655	14	11586	4
	fir	1529	9	1007	12	445	16	76	52	72	36	44	46	3173	6
	pine	379	17	48	68	8	100	65	37	159	33	14	71	673	14
	larch	75	37	70	42	48	48	678	16	507	17	296	19	1674	9
	Arolla pine	0	.	7	100	0	.	26	60	99	34	0	.	132	28
	other conifers	70	51	0	.	0	.	0	.	0	.	0	.	70	51
	beech	1879	8	561	18	451	19	7	100	65	45	158	27	3122	6
	maple	134	26	29	51	26	59	0	.	14	71	0	.	202	21
	ash	237	21	30	60	37	52	0	.	0	.	11	100	316	18
	oak	337	20	0	.	6	100	0	.	0	.	11	100	354	19
	sweet chestnut	0	.	0	.	0	.	7	100	6	100	164	29	177	27
	other broadleaves	67	37	77	43	35	54	15	100	0	.	8	100	202	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8660	4	4719	6	2852	8	1602	11	2486	8	1360	9	21679	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1128	10	944	11	733	15	250	23	500	15	281	23	3836	6
	fir	433	15	349	18	140	25	15	71	29	51	30	49	996	10
	pine	63	37	11	100	7	100	0	.	35	53	0	.	116	28
	larch	14	71	16	71	30	51	230	22	133	27	121	26	543	13
	Arolla pine	0	.	0	.	0	.	17	72	22	58	0	.	39	45
	other conifers	28	61	0	.	0	.	0	.	0	.	0	.	28	61
	beech	435	15	131	26	51	46	0	.	18	74	84	32	719	12
	maple	14	71	6	100	6	100	0	.	0	.	0	.	27	50
	ash	84	33	7	100	7	100	0	.	12	100	0	.	111	29
	oak	51	38	0	.	12	100	0	.	0	.	0	.	63	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	63	35	63	35
	other broadleaves	45	48	15	72	0	.	0	.	0	.	9	100	69	37
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2294	7	1479	9	987	13	512	16	751	12	587	13	6610	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	246	18	199	21	337	17	85	35	186	21	134	28	1187	9
	fir	186	21	123	28	25	50	6	100	21	58	15	71	376	15
	pine	7	100	0	.	0	.	0	.	14	71	0	.	21	58
	larch	14	71	7	100	0	.	135	30	92	32	36	52	283	19
	Arolla pine	0	.	0	.	0	.	28	50	22	58	0	.	50	38
	other conifers	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	beech	186	23	39	52	10	100	0	.	0	.	23	58	257	19
	maple	0	.	0	.	17	73	0	.	0	.	0	.	17	73
	ash	7	100	0	.	7	100	0	.	0	.	0	.	14	71
	oak	46	41	0	.	8	100	0	.	0	.	0	.	54	38
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	77	36	85	34
	other broadleaves	8	100	0	.	0	.	0	.	0	.	7	100	15	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	698	11	375	15	404	16	262	21	335	16	292	18	2366	6
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	73	32	59	35	55	39	34	45	41	41	14	71	276	16
	fir	44	41	62	36	7	100	0	.	0	.	25	58	138	24
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	29	50	14	71	7	100	50	38
	Arolla pine	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	36	60	8	100	15	71	7	100	6	100	0	.	72	38
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	17	72	0	.	0	.	0	.	0	.	0	.	17	72
	oak	29	63	0	.	0	.	0	.	0	.	0	.	29	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	73	50	73	50
	other broadleaves	24	73	0	.	0	.	0	.	0	.	0	.	24	73
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	222	20	129	24	77	32	78	33	61	33	120	34	687	12
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	22	58	0	.	65	33	15	71	7	100	11	100	120	25
	fir	7	100	7	100	9	100	0	.	0	.	0	.	23	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	0	.	7	100	21	58
	Arolla pine	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	22	58	0	.	0	.	0	.	0	.	0	.	22	58
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	31	50	31	50
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	58	35	7	100	73	32	36	45	7	100	49	41	231	18
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	13	71	0	.	0	.	17	72	8	100	38	45
	fir	0	.	0	.	0	.	0	.	0	.	10	100	10	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	7	100	7	100	14	71
	Arolla pine	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	61	46	61	46
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	13	71	0	.	7	100	31	51	86	36	138	27
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	62994	5	49634	6	30075	9	18949	10	30914	7	9710	11	202275	3
	fir	28970	6	14835	8	7126	11	1358	27	892	26	1468	26	54648	4
	pine	5857	11	1134	42	553	45	6639	19	5092	18	312	55	19587	9
	larch	1158	20	316	31	503	45	8615	13	4558	12	5102	14	20253	7
	Arolla pine	0	.	22	58	0	.	1432	25	2483	20	16	100	3954	15
	other conifers	1452	22	78	47	209	40	277	82	264	51	8	100	2288	18
	beech	51878	4	15581	10	12306	10	2026	30	2018	25	10888	14	94696	3
	maple	9124	7	4753	12	4115	13	835	31	455	35	532	29	19814	5
	ash	10304	9	2858	18	3787	18	711	30	655	60	1333	25	19649	7
	oak	7991	13	593	34	174	44	655	34	84	74	2264	26	11760	11
	sweet chestnut	17	100	0	.	0	.	176	61	28	100	13761	13	13982	12
	other broadleaves	11584	9	5235	12	4486	17	4074	17	3890	22	14209	10	43479	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	191328	2	95039	4	63334	6	45746	7	51332	6	59604	5	506383	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1358350. Birmensdorf, Swiss Federal Research Institute WSL

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